arXiv subjects

Pedro Simões

Publications and source records attributed to Pedro Simões.

3 recordsLinked to original sources

arXiv2025

Agentic LMs: Hunting Down Test Smells

Test smells reduce test suite reliability and complicate maintenance. While many methods detect test smells, few support automated removal, and most rely on static analysis or machine learning. This study evaluates models with relatively small parameter counts - Llama-3.2-3B, Gemma-2-9B, DeepSeek-R1-14B, and Phi-4-14B - for their ability to detect and refactor test smells using agent-based workflows. We assess workflows with one, two, and four agents over 150 instances of 5 common smells from real-world Java projects. Our approach generalizes to Python, Golang, and JavaScript. All models detected nearly all instances, with Phi-4-14B achieving the best refactoring accuracy (pass@5 of 75.3%). Phi-4-14B with four-agents performed within 5% of proprietary LLMs (single-agent). Multi-agent setups outperformed single-agent ones in three of five smell types, though for Assertion Roulette, one agent sufficed. We submitted pull requests with Phi-4-14B-generated code to open-source projects and six were merged.

cs.SE↗

arXiv2025

RefModel: Detecting Refactorings using Foundation Models

Refactoring is a common software engineering practice that improves code quality without altering program behavior. Although tools like ReExtractor+, RefactoringMiner, and RefDiff have been developed to detect refactorings automatically, they rely on complex rule definitions and static analysis, making them difficult to extend and generalize to other programming languages. In this paper, we investigate the viability of using foundation models for refactoring detection, implemented in a tool named RefModel. We evaluate Phi4-14B, and Claude 3.5 Sonnet on a dataset of 858 single-operation transformations applied to artificially generated Java programs, covering widely-used refactoring types. We also extend our evaluation by including Gemini 2.5 Pro and o4-mini-high, assessing their performance on 44 real-world refactorings extracted from four open-source projects. These models are compared against RefactoringMiner, RefDiff, and ReExtractor+. RefModel is competitive with, and in some cases outperform, traditional tools. In real-world settings, Claude 3.5 Sonnet and Gemini 2.5 Pro jointly identified 97% of all refactorings, surpassing the best-performing static-analysis-based tools. The models showed encouraging generalization to Python and Golang. They provide natural language explanations and require only a single sentence to define each refactoring type.

cs.SE↗

arXiv2020

TACO: Trash Annotations in Context for Litter Detection

TACO is an open image dataset for litter detection and segmentation, which is growing through crowdsourcing. Firstly, this paper describes this dataset and the tools developed to support it. Secondly, we report instance segmentation performance using Mask R-CNN on the current version of TACO. Despite its small size (1500 images and 4784 annotations), our results are promising on this challenging problem. However, to achieve satisfactory trash detection in the wild for deployment, TACO still needs much more manual annotations. These can be contributed using: http://tacodataset.org/

cs.CV↗